This invention relates to digital image processing. More particularly, it relates a method for correcting artefacts originating from non-linear treatment of components (coefficients) of a multi-scale image representation.
The method is applicable e.g. in a medical radiographic system, such as a computed radiography (CR) system or a computed tomography (CT) system.
In imaging systems where the final output image has to be evaluated by a human observer, a problem arises if the original image, as obtained from an image sensing device, contains detail information at various degrees of coarseness, within a wide amplitude range. This situation is typical when the sensor has a good signal to noise ratio over a large dynamic range, which is the case with CR and CT. In common working environments such as a hospital there is no time for selecting the optimal window/level setting (if any) for each individual image, so there is a need to display a single image on film or monitor screen, which reveals all the relevant diagnostic details with an acceptable contrast over the whole dynamic range. This problem has been recognized in the field of digital radiography, see:
Mack I., Heitzel U., Optimized image processing for routine digital radiography. Proceedings International Symposium CAR, 191, p. 109, Springer Verlag.
Many methods for contrast enhancement have been developed, such as the commonly known techniques of unsharp masking (Cocklin, M. L., Gourlay A. R., Jackson P. H., Kaye G., Kerr I. H., and Lams P., Digital processing of chest radiographs, Image and Vision Computing, Vol. 1, No. 21, 67-68 (1983)) and adaptive histogram modification (Pizer S. M., Amburn E. P., Austin J. D., Cromartie R., Geselowitz A., Greer T., Ter Haar Romery B., Zimmerman J. B., and Zuiderveld K., Adaptive histogram equalisation and its variations, Computer Vision, Graphics, and Image Processing, Vol. 39, 355-368 (1987)). These traditional methods suffer from two problems:
1. They fix to a particular filter size, whereas diagnostic details may occur at multiple scales in one and the same image. It may be desired to enhance details in the image at every possible scale.
2. Artefacts are created in the vicinity of significant signal level transitions, e.g. a bone/soft tissue boundaries within the image. These artificats may impair the diagnosis.
To overcome the first problem, multiscale methods for contrast enhancement have been developed in the past few years, see Vuylsteke P., Schoeters E., Multiscale Image Contrast Amplification (MUSICA), Proceedings of SPIE, Vol. 2167, 5510560 (1994) and Lu J., Healy D. M. Jr., Contrast enhancement via multiscale gradient transformation, In Proceedings of SPIE: Wavelet applications, Orlando, Fla. (1994); Fan J., Laine A., Contrast enhancement by multiscale and nonlinear operators, in Wavelets in medicine and biology, Akram Aldroubi and Michael Unser eds., CRC Press (1996).
While effectively curing this first problem, these methods also seem to perform well with regard to the second problem. The MUSICA algorithm is being used routinely in several hospitals around the world and there are no complaints about artefacts in CR. We have however noted that one has to be more careful in using these methods on CT images, since they have sharper signal level transitions and artefacts might still be created there.
It is an object of the present invention to provide a method which corrects for artefacts i.a. originating from nonlinear treatment of components (coefficients) in a multi-scale representation.
Further objects of the present invention will become apparent from the description hereafter.
The objects of the present invention are achieved by a method as set forth in claim 1.
The method of this invention is based on a multiscale gradient representation such as described in: Mallat S., Zhong S., Characterization of signals from multiscale edges, IEEE Trans. on Pattern analysis and Machine Intelligence, Vol. 14, No. 7 (1992)).
The method has two basic steps. The first step is the nonlinear processing step, e.g. a contrast enhancement step (e.g. using an algorithm similar to that of Lu and Healy (see above)). The second and novel part is an artefact correcting step. This step can be used to correct artefacts caused by any nonlinear treatment of the coefficients in the multiscale gradient representation and its use is therefore not necessarily restricted to the problem of contrast enhancement.